nobedroom.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## HOUSE PRICE PREDICTION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing = pd.read_csv(\"kathmandudata.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bedroom</th>\n",
       "      <th>bathroom</th>\n",
       "      <th>floors</th>\n",
       "      <th>parking</th>\n",
       "      <th>location</th>\n",
       "      <th>dhur</th>\n",
       "      <th>Aana</th>\n",
       "      <th>Paisa</th>\n",
       "      <th>Daam</th>\n",
       "      <th>roadsize(feet)</th>\n",
       "      <th>Road type</th>\n",
       "      <th>Years ago</th>\n",
       "      <th>Area(sq ft)</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>Chyasingdol, Pasikot, Kathmandu</td>\n",
       "      <td>0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Soil Stabilized</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2566.87</td>\n",
       "      <td>36000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1</td>\n",
       "      <td>Nearby, Buddha party palace, Imadol, Lalitpur</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Gravelled</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1026.75</td>\n",
       "      <td>18000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Nayabasti, Imadol, Lalitpur</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Gravelled</td>\n",
       "      <td>2.0</td>\n",
       "      <td>855.62</td>\n",
       "      <td>15000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>Rudreshwor, Budhanilkantha, Kathmandu</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.0</td>\n",
       "      <td>Blacktopped</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2187.00</td>\n",
       "      <td>57000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3</td>\n",
       "      <td>Bafal, Bafal, Kathmandu</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Blacktopped</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1540.12</td>\n",
       "      <td>24500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   bedroom  bathroom  floors  parking  \\\n",
       "0        0         0     NaN        0   \n",
       "1        4         3     2.5        1   \n",
       "2        4         3     3.0        0   \n",
       "3        7         5     2.0        3   \n",
       "4        7         3     2.5        3   \n",
       "\n",
       "                                        location dhur  Aana  Paisa  Daam  \\\n",
       "0                Chyasingdol, Pasikot, Kathmandu    0   7.0    2.0   0.0   \n",
       "1  Nearby, Buddha party palace, Imadol, Lalitpur    0   3.0    0.0   0.0   \n",
       "2                    Nayabasti, Imadol, Lalitpur    0   2.0    2.0   0.0   \n",
       "3          Rudreshwor, Budhanilkantha, Kathmandu   12   NaN    NaN   NaN   \n",
       "4                        Bafal, Bafal, Kathmandu    0   4.0    2.0   0.0   \n",
       "\n",
       "   roadsize(feet)        Road type  Years ago  Area(sq ft)     Price  \n",
       "0            20.0  Soil Stabilized        2.0      2566.87  36000000  \n",
       "1            13.0        Gravelled        2.0      1026.75  18000000  \n",
       "2            13.0        Gravelled        2.0       855.62  15000000  \n",
       "3            15.0      Blacktopped        1.0      2187.00  57000000  \n",
       "4            13.0      Blacktopped        1.0      1540.12  24500000  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dropping unnecessary columns\n",
    "1. Location is out of our scope, so it is dropped.\n",
    "2. Dhur, Aana, Paisa and Daam is dropped as we have already converted them into square feet."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "\"['location', 'dhur', 'Aana', 'Paisa', 'Daam', 'Years ago'] not found in axis\"",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Input \u001b[1;32mIn [63]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mhousing\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdrop\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mlocation\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mdhur\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mAana\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mPaisa\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mDaam\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mYears ago\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minplace\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\util\\_decorators.py:311\u001b[0m, in \u001b[0;36mdeprecate_nonkeyword_arguments.<locals>.decorate.<locals>.wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    305\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(args) \u001b[38;5;241m>\u001b[39m num_allow_args:\n\u001b[0;32m    306\u001b[0m     warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[0;32m    307\u001b[0m         msg\u001b[38;5;241m.\u001b[39mformat(arguments\u001b[38;5;241m=\u001b[39marguments),\n\u001b[0;32m    308\u001b[0m         \u001b[38;5;167;01mFutureWarning\u001b[39;00m,\n\u001b[0;32m    309\u001b[0m         stacklevel\u001b[38;5;241m=\u001b[39mstacklevel,\n\u001b[0;32m    310\u001b[0m     )\n\u001b[1;32m--> 311\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m func(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\frame.py:4948\u001b[0m, in \u001b[0;36mDataFrame.drop\u001b[1;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[0;32m   4800\u001b[0m \u001b[38;5;129m@deprecate_nonkeyword_arguments\u001b[39m(version\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, allowed_args\u001b[38;5;241m=\u001b[39m[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mself\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlabels\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n\u001b[0;32m   4801\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdrop\u001b[39m(\n\u001b[0;32m   4802\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   4809\u001b[0m     errors: \u001b[38;5;28mstr\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mraise\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m   4810\u001b[0m ):\n\u001b[0;32m   4811\u001b[0m     \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m   4812\u001b[0m \u001b[38;5;124;03m    Drop specified labels from rows or columns.\u001b[39;00m\n\u001b[0;32m   4813\u001b[0m \n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   4946\u001b[0m \u001b[38;5;124;03m            weight  1.0     0.8\u001b[39;00m\n\u001b[0;32m   4947\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m-> 4948\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdrop\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   4949\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlabels\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlabels\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   4950\u001b[0m \u001b[43m        \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   4951\u001b[0m \u001b[43m        \u001b[49m\u001b[43mindex\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mindex\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   4952\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   4953\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlevel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlevel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   4954\u001b[0m \u001b[43m        \u001b[49m\u001b[43minplace\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minplace\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   4955\u001b[0m \u001b[43m        \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   4956\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\generic.py:4279\u001b[0m, in \u001b[0;36mNDFrame.drop\u001b[1;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[0;32m   4277\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m axis, labels \u001b[38;5;129;01min\u001b[39;00m axes\u001b[38;5;241m.\u001b[39mitems():\n\u001b[0;32m   4278\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m labels \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m-> 4279\u001b[0m         obj \u001b[38;5;241m=\u001b[39m \u001b[43mobj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_drop_axis\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlabels\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlevel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlevel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   4281\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m inplace:\n\u001b[0;32m   4282\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_update_inplace(obj)\n",
      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\generic.py:4323\u001b[0m, in \u001b[0;36mNDFrame._drop_axis\u001b[1;34m(self, labels, axis, level, errors, consolidate, only_slice)\u001b[0m\n\u001b[0;32m   4321\u001b[0m         new_axis \u001b[38;5;241m=\u001b[39m axis\u001b[38;5;241m.\u001b[39mdrop(labels, level\u001b[38;5;241m=\u001b[39mlevel, errors\u001b[38;5;241m=\u001b[39merrors)\n\u001b[0;32m   4322\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 4323\u001b[0m         new_axis \u001b[38;5;241m=\u001b[39m \u001b[43maxis\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdrop\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlabels\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   4324\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m axis\u001b[38;5;241m.\u001b[39mget_indexer(new_axis)\n\u001b[0;32m   4326\u001b[0m \u001b[38;5;66;03m# Case for non-unique axis\u001b[39;00m\n\u001b[0;32m   4327\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\indexes\\base.py:6644\u001b[0m, in \u001b[0;36mIndex.drop\u001b[1;34m(self, labels, errors)\u001b[0m\n\u001b[0;32m   6642\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m mask\u001b[38;5;241m.\u001b[39many():\n\u001b[0;32m   6643\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m errors \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mignore\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m-> 6644\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlist\u001b[39m(labels[mask])\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m not found in axis\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m   6645\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m indexer[\u001b[38;5;241m~\u001b[39mmask]\n\u001b[0;32m   6646\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdelete(indexer)\n",
      "\u001b[1;31mKeyError\u001b[0m: \"['location', 'dhur', 'Aana', 'Paisa', 'Daam', 'Years ago'] not found in axis\""
     ]
    }
   ],
   "source": [
    "housing.drop(['bedroom','location', 'dhur', 'Aana', 'Paisa', 'Daam', 'Years ago'], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "housing.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "bathroom                  0\n",
       "floors                  NaN\n",
       "parking                   0\n",
       "roadsize(feet)         12.0\n",
       "Road type         Gravelled\n",
       "Area(sq ft)         1090.92\n",
       "Price              11500000\n",
       "Name: 6, dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.loc[6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.00    552\n",
       "2.00    127\n",
       "2.50    113\n",
       "4.00     57\n",
       "1.00     49\n",
       "1.50     23\n",
       "3.50     14\n",
       "5.00      7\n",
       "6.00      3\n",
       "0.00      2\n",
       "1.35      1\n",
       "Name: floors, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing['floors'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    685\n",
       "3    259\n",
       "4    245\n",
       "5    163\n",
       "6     90\n",
       "2     81\n",
       "1     75\n",
       "7     22\n",
       "8      4\n",
       "Name: bathroom, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing['bathroom'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bathroom</th>\n",
       "      <th>floors</th>\n",
       "      <th>parking</th>\n",
       "      <th>roadsize(feet)</th>\n",
       "      <th>Area(sq ft)</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1624.000000</td>\n",
       "      <td>948.000000</td>\n",
       "      <td>1624.000000</td>\n",
       "      <td>1624.000000</td>\n",
       "      <td>1624.000000</td>\n",
       "      <td>1.624000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.176724</td>\n",
       "      <td>2.750369</td>\n",
       "      <td>0.998768</td>\n",
       "      <td>12.255429</td>\n",
       "      <td>1369.061418</td>\n",
       "      <td>2.659940e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.164561</td>\n",
       "      <td>0.712053</td>\n",
       "      <td>1.310651</td>\n",
       "      <td>5.866296</td>\n",
       "      <td>663.209911</td>\n",
       "      <td>1.360174e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.800000e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>962.570000</td>\n",
       "      <td>1.700000e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>1197.870000</td>\n",
       "      <td>2.350000e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>1619.920000</td>\n",
       "      <td>3.300000e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>6014.250000</td>\n",
       "      <td>7.000000e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          bathroom      floors      parking  roadsize(feet)  Area(sq ft)  \\\n",
       "count  1624.000000  948.000000  1624.000000     1624.000000  1624.000000   \n",
       "mean      2.176724    2.750369     0.998768       12.255429  1369.061418   \n",
       "std       2.164561    0.712053     1.310651        5.866296   663.209911   \n",
       "min       0.000000    0.000000     0.000000        0.000000     0.000000   \n",
       "25%       0.000000    2.500000     0.000000       12.000000   962.570000   \n",
       "50%       2.000000    3.000000     1.000000       13.000000  1197.870000   \n",
       "75%       4.000000    3.000000     2.000000       15.000000  1619.920000   \n",
       "max       8.000000    6.000000     8.000000       25.000000  6014.250000   \n",
       "\n",
       "              Price  \n",
       "count  1.624000e+03  \n",
       "mean   2.659940e+07  \n",
       "std    1.360174e+07  \n",
       "min    1.800000e+06  \n",
       "25%    1.700000e+07  \n",
       "50%    2.350000e+07  \n",
       "75%    3.300000e+07  \n",
       "max    7.000000e+07  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'bathroom'}>,\n",
       "        <AxesSubplot:title={'center':'floors'}>],\n",
       "       [<AxesSubplot:title={'center':'parking'}>,\n",
       "        <AxesSubplot:title={'center':'roadsize(feet)'}>],\n",
       "       [<AxesSubplot:title={'center':'Area(sq ft)'}>,\n",
       "        <AxesSubplot:title={'center':'Price'}>]], dtype=object)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# # For plotting histogram\n",
    "import matplotlib.pyplot as plt\n",
    "housing.hist(bins=50, figsize=(20, 15))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1624 entries, 0 to 1623\n",
      "Data columns (total 7 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   bathroom        1624 non-null   int64  \n",
      " 1   floors          948 non-null    float64\n",
      " 2   parking         1624 non-null   int64  \n",
      " 3   roadsize(feet)  1624 non-null   float64\n",
      " 4   Road type       1624 non-null   object \n",
      " 5   Area(sq ft)     1624 non-null   float64\n",
      " 6   Price           1624 non-null   int64  \n",
      "dtypes: float64(3), int64(3), object(1)\n",
      "memory usage: 88.9+ KB\n"
     ]
    }
   ],
   "source": [
    "housing.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Filling Missing values\n",
    "1. Filling N/A values of floors\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.0\n"
     ]
    }
   ],
   "source": [
    "housing=housing.replace('N/A', np.nan)\n",
    "avgfloors=housing['floors'].mean().round(0)\n",
    "#avgbedroom=housing['bedroom'].mean().round(0)\n",
    "print(avgfloors)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing['floors'] = housing['floors'].replace(np.nan, avgfloors)\n",
    "#housing['bedroom']=housing['bedroom'].replace(0,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bathroom                  0\n",
      "floors                  3.0\n",
      "parking                   0\n",
      "roadsize(feet)         12.0\n",
      "Road type         Gravelled\n",
      "Area(sq ft)         1090.92\n",
      "Price              11500000\n",
      "Name: 6, dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(housing.loc[6])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16000000.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q3, q1 = np.percentile(housing['Price'], [75 ,25])\n",
    "iqr=q3-q1\n",
    "iqr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Label Encoding: One-Hot Encoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#from sklearn.preprocessing import OneHotEncoder\n",
    "# creating instance of one-hot-encoder\n",
    "#enc = OneHotEncoder(handle_unknown='ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#dum_df = pd.get_dummies(housing[['Road type']],prefix=[\"Road\"] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#housing = housing.join(dum_df)\n",
    "#housing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Label Encoding:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bathroom</th>\n",
       "      <th>floors</th>\n",
       "      <th>parking</th>\n",
       "      <th>roadsize(feet)</th>\n",
       "      <th>Road type</th>\n",
       "      <th>Area(sq ft)</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5</td>\n",
       "      <td>2566.87</td>\n",
       "      <td>36000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1</td>\n",
       "      <td>13.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1026.75</td>\n",
       "      <td>18000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>3</td>\n",
       "      <td>855.62</td>\n",
       "      <td>15000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2187.00</td>\n",
       "      <td>57000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1540.12</td>\n",
       "      <td>24500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1619</th>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>5</td>\n",
       "      <td>1112.31</td>\n",
       "      <td>18300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1620</th>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1197.87</td>\n",
       "      <td>16500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1621</th>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>1711.25</td>\n",
       "      <td>27500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1622</th>\n",
       "      <td>5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
       "      <td>13.0</td>\n",
       "      <td>5</td>\n",
       "      <td>1754.03</td>\n",
       "      <td>34000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1623</th>\n",
       "      <td>4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>14.0</td>\n",
       "      <td>5</td>\n",
       "      <td>2258.00</td>\n",
       "      <td>27000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1624 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      bathroom  floors  parking  roadsize(feet)  Road type  Area(sq ft)  \\\n",
       "0            0     3.0        0            20.0          5      2566.87   \n",
       "1            3     2.5        1            13.0          3      1026.75   \n",
       "2            3     3.0        0            13.0          3       855.62   \n",
       "3            5     2.0        3            15.0          1      2187.00   \n",
       "4            3     2.5        3            13.0          1      1540.12   \n",
       "...        ...     ...      ...             ...        ...          ...   \n",
       "1619         0     3.0        0            13.0          5      1112.31   \n",
       "1620         0     1.0        0            10.0          4      1197.87   \n",
       "1621         0     3.0        0             0.0          5      1711.25   \n",
       "1622         5     3.0        2            13.0          5      1754.03   \n",
       "1623         4     2.0        1            14.0          5      2258.00   \n",
       "\n",
       "         Price  \n",
       "0     36000000  \n",
       "1     18000000  \n",
       "2     15000000  \n",
       "3     57000000  \n",
       "4     24500000  \n",
       "...        ...  \n",
       "1619  18300000  \n",
       "1620  16500000  \n",
       "1621  27500000  \n",
       "1622  34000000  \n",
       "1623  27000000  \n",
       "\n",
       "[1624 rows x 7 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labelencoder = LabelEncoder()\n",
    "housing['Road type'] = labelencoder.fit_transform(housing['Road type'])\n",
    "housing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5    700\n",
       "1    411\n",
       "3    408\n",
       "4     83\n",
       "2     19\n",
       "0      3\n",
       "Name: Road type, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing['Road type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.00    1228\n",
       "2.00     127\n",
       "2.50     113\n",
       "4.00      57\n",
       "1.00      49\n",
       "1.50      23\n",
       "3.50      14\n",
       "5.00       7\n",
       "6.00       3\n",
       "0.00       2\n",
       "1.35       1\n",
       "Name: floors, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing['floors'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bathroom</th>\n",
       "      <th>floors</th>\n",
       "      <th>parking</th>\n",
       "      <th>roadsize(feet)</th>\n",
       "      <th>Road type</th>\n",
       "      <th>Area(sq ft)</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5</td>\n",
       "      <td>2566.87</td>\n",
       "      <td>36000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1</td>\n",
       "      <td>13.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1026.75</td>\n",
       "      <td>18000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>3</td>\n",
       "      <td>855.62</td>\n",
       "      <td>15000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2187.00</td>\n",
       "      <td>57000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1540.12</td>\n",
       "      <td>24500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   bathroom  floors  parking  roadsize(feet)  Road type  Area(sq ft)     Price\n",
       "0         0     3.0        0            20.0          5      2566.87  36000000\n",
       "1         3     2.5        1            13.0          3      1026.75  18000000\n",
       "2         3     3.0        0            13.0          3       855.62  15000000\n",
       "3         5     2.0        3            15.0          1      2187.00  57000000\n",
       "4         3     2.5        3            13.0          1      1540.12  24500000"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Normalization vs Standardization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import pandas as pd\n",
    "# from sklearn import preprocessing\n",
    "\n",
    "# x = housing.values #returns a numpy array\n",
    "# min_max_scaler = preprocessing.MinMaxScaler()\n",
    "# x_scaled = min_max_scaler.fit_transform(x)\n",
    "# housing = pd.DataFrame(x_scaled,columns = housing.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# from sklearn.preprocessing import StandardScaler\n",
    "# object= StandardScaler()\n",
    "\n",
    "# # standardization \n",
    "# x_scaled= object.fit_transform(housing) \n",
    "# housing = pd.DataFrame(x_scaled,columns = housing.columns)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "#housing.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train-Test Splitting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sklearn train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# from sklearn.model_selection import train_test_split\n",
    "# train_set, test_set  = train_test_split(housing, test_size=0.2, random_state=42)\n",
    "# print(f\"Rows in train set: {len(train_set)}\\nRows in test set: {len(test_set)}\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import StratifiedShuffleSplit\n",
    "split = StratifiedShuffleSplit(n_splits=1, test_size=0.2, random_state=42)\n",
    "for train_index, test_index in split.split(housing,housing['bathroom']):\n",
    "     strat_train_set = housing.loc[train_index]\n",
    "     strat_test_set = housing.loc[test_index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    137\n",
       "3     52\n",
       "4     49\n",
       "5     33\n",
       "6     18\n",
       "2     16\n",
       "1     15\n",
       "7      4\n",
       "8      1\n",
       "Name: bathroom, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " strat_test_set['bathroom'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    548\n",
       "3    207\n",
       "4    196\n",
       "5    130\n",
       "6     72\n",
       "2     65\n",
       "1     60\n",
       "7     18\n",
       "8      3\n",
       "Name: bathroom, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "strat_train_set['bathroom'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# strat_train_set['bedroom'].value_counts()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Looking for Correlations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Price             1.000000\n",
       "Area(sq ft)       0.444660\n",
       "bathroom          0.410562\n",
       "parking           0.409572\n",
       "roadsize(feet)    0.252386\n",
       "floors            0.202923\n",
       "Road type        -0.255826\n",
       "Name: Price, dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix = housing.corr()\n",
    "corr_matrix['Price'].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Relationship"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'Median Price of Houses'),\n",
       " Text(0.5, 0, 'Price (NRS)'),\n",
       " Text(0, 0.5, 'Bedroom')]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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KfI8eQEdHsn1l5eA33zx8vaOPBiZMAH72M3XxVY4KvFK7GIFTB59sO4bFi4GBA4sPR0AEXHop8N57wG23pbNvJRNU4JXapZYjmmp18FGHND72WGD33cXFp1HTr2SCCrxSu1RzROO9ktVLmg6+EgJvsvh335VJxpWqRAVeqV2q1cE3NoqAh9GjR3GBX7kyfNyaLCKaoBJJP447DujTB3j55XT2r6SOCrxSu1SjwK9dG23+1fr68Ihm9WpgxAjgnnuC10m7Dj6OgwfExfftW13vv1KACrxSu1QqounokMm0V6zo+pzfSJJ+FHPwK1cC69YB778fvE6aEc3atfIXd1rBpiZpp1KVqMArtUulqmjeeEPGZfGrIIkq8MUcvBHtsGNLM6IpNkxBEL17q8BXMSrwSu1SqYjGDCjmlz2n5eCNaIeJZ5oOvtgwBUE0NWlEU8WowCu1S6UimpYWufWbXrISDj4NgY9yFasfGtFUNZkKPBENIKJpRPQWEc0hon2y3J/SzaiUgzeCNmeOK/aGSjj4NCKaUgVeI5qqJmsH/1sAjzLzTgB2AzAn4/0peWPjRrmy8vbb/Z8Dyi/wRtQ7O4GZMwufq2UHX1cHDB0a73Ua0VQ1mQk8EfUHcACAPwMAM7cy88qs9qfUIJ2dwGOPhdd6L1okQnbxxV2fq3REA3SNadJy8Ea0w9yxOf60OlmHDJF2xUEjmqomSwe/DYClAG4moleJ6E9E1Me7EhGdQUQziGiGzuLSzXjuOZlEwuuCbYwI+glPpSOahgaJaQzM0gFb7CpWQKINb7xjU6xCiNk9CaTl4OPGM4BGNFVOlgJfD2APAH9g5t0BtADoYsOY+UZmnsjME4fG/Xmo1DZGGFatCl7HxBh+Al+pMkkjzJtv7lbUmOXM0Rz86NEycUYQxRy8LeqVFHiNaKqaLAW+GUAzM7/kPJ4GEXxFKcQbMbS2ymxNgOvg/S79r2REU1cHDB5cKPBh87F62WYbEdUgAS928rLfs7Q6WeOWSAIa0VQ5mQk8My8C8CERjXEWHQLgzaz2p9QgJns3Qm046yxgxx1lKr4oAh/VQb7+OvDgg6W11WbdOhmDpW/fQoGPOpIkIAIPBLv4Yg7eFvWkDp5ZMvhSI5rW1uQjYyqZkPWk22cDuI2IegJ4D8BXM96fUksECfz06XK7Zk14RBNH4JubgUMOEUE65pjS2mtoaXEF3o6X4gj8ttvK7XvvAbvs0vX5OA4+qcCvWiXvZakRDSDtjNL3oJSVTAWemWcCmJjlPpQc4I0YjPATuQKfJKJpbQU+8xlg6VJg+PBkbQVE4JuaROQXLHCXl+Lg583zfz6Og08a0ZRaAw+4Ar9unQp8FaJXsiqVI8jB2wKfRhXN978PvPiiiGpYSWZUbAdfakSz+eYiju+95/98sTr4NB18qcMUAPKLCNAcvkpRgVcqT5DAd3a6Dv4//+man0epornzTuD3vwfOPx849NBkAm9GkWxuTi7wRHLCCXLwxa5ktd+zanHwStWhAq9UDiO2QRFNe3th591Pf1q4nh3RBAn3vffKuOq/+IWIahKBN6NIvvyyG9HYtexxBB6QHD6Kg/drc5oOPg2B11LJqkQFXqkcQRGNob298GpPb6WG/bqgbbS2AgMGSIafVOBXrnTvGwe/fr3brrgCbxx8MQH362NIW+AbGmTC7bhoRFPVqMArlScoomlrKxT1zs7CdTZudMU0LKtuaJD7SQV+9Wr3vhF4wHXxRuD7dLlg259tt5WIZ9myrs/Zou0nnkbgiZJHNIsXS59AXQlyoBFNVaMCr1SOuBGNLfDt7bJe//7yOKiSpq0N6NlT7icVeLsksk8fV8htgW9qij6eS1gljf2e+J28zPNNTek4+FLiGbN/QCOaKkUFXqkcxapowgTevGbAALkNEpi2tvQcvC3wvXu7Dt50tEadj9Vg18J7ierg+/SprMBrRFPVqMArlSdM4O0MvhSB90Y09jbiYkc09qBiRuCjjiRpGD1abv0cvC3aYQ6+b990qmhKKZEENKKpclTglcqRJKIxr6lURLNwoX9EE0fg+/aV7NvPwdvvSZiD79s3mYPv7ASWLNGIJqeowNc6d98NHH10pVsRj7vuAo49Np2Ixgh80ojm3HOlZj4MW+Db2pI7eEBy+GIRjd+xmeNPKvDLl8t7XKqDzzKiWblShpN+U4ewKhUV+FrnpJOAhx+udCvicfLJhRctlVImmXYGf8stwEMPhbd79WqJVX76U2DKlHQEfsAA/3Hhozr4Pn2SRTQffCC3W21V2usbG+U2C4G/+26ZEOZf/0p/290EFXilchhHXkpE43XwQRFNa2vxiIZZxNvO2P1YtUoE+Yc/BEaNSh7RhBE1g0/ayWpGszT9AXGpqxMXn0VEM22a3IbNF6CEogKvVA4jUpWuolm/XrYdReDNCQVIx8EDwRc6mbr0Yhl8EgdvBN6UbJZCFrM6ffwx8MQTcl8FvmRU4JXKUUzg29qSV9FEEXhzgVIxgV+9GthsM/dxGgJP5L+8rS28fyEtBz9vnuzHvI+lkMWkH/ff7x6XCnzJZD0evFIumIPFotqorxfhTuLgo1bR2BFNXV24wJvbILwOvndvec9bWqRtLS2lDZnr1yYj8B9/nG0d/Pz5pcczhiym7Zs6VWIw5sIhIpRYqIPPC2kMg1sujKM2wh6WwdviVQ0O3hZ4IhHYtWvjTddnE3RSbm2N5+BL/fznzUsWzwDpRzSrVgGPPy6d8QMGqINPgAp8XqhFgY/i4ItFNGmUSRphDxN4ZhEaO6IB3CGDSxX4INraRLzr6vzFc+NG+WVifp3Y71NUmNNz8GkKvIlnTj5ZPl8V+JJRgc8LeRJ4Q5QySSOoSapojINvaQmeW3TdOnnOdvCAO2Rw3JEkbYI6WRsaguMPc1zmvSwlplm2TI4rqYNPO6KZNk3KNvfeWx18QlTg80KSS/DLjZl+L0pEU8zBNzbKXxoRDVA4gYeNcfdegTcOvlSBD+tk7dkzOP4wAm9OXqVU0pghEpI6+DQjmtWrpfb9pJPkvenfXzP4BKjA54U8Ofg4At+rV3AdtpkRKo7AB8U0xkUGRTS16OCT1sAb0oxoHnxQPtvPfEYea0STCBX4vFArAn/11TLlHZBM4M1re/USB+8X0RjRixrRAMUFPu2IJi0HX2mBTyuimTpVJkafNEke9+8vn0mtfL+rDBX4vFAr/wD//rd734i0N14wordxYzIHb0QvaicrEFwqmVVEAwSXScZx8KVGNIMGdf1VEpe0Ipo1a4BHHpF4xlzkNWCA/ArzG85BKUqmdfBENB/AGgAdANqZeWKW++vW1EoGbzvNIAdvXOnSpW5eD4QLfJiDr+aIJqxMsqGhuINPGtEkde9AehHNww8XxjOAe0JdubK0awy6OeVw8Acx83gV94ypFQdvC5ERaa/Am3UWLSru4BsagjtZzQkkTYHPoorGDxPRhDn4Xr2SdbLOn5+8ggZwBT7pd3DqVBm2eN993WXm/dYcviQ0oskLtSjwRpTa2wvF26yzeHF4mWRDQ/hgV3Ey+F695H6QwEeNaMz46HFglvdi7lx3md3JGlYHX6qDT6sGHnCHDA4qd41CS4s4+JNOKpzyUAU+EVkLPAN4nIheJqIz/FYgojOIaAYRzVi6dGnGzckxtSzw3vtRHbwR5TgRjR9r1gAjRrj3/TAC43XoffvKvk2EEHfiatOms84CdtpJYinTdtPJmkUVzeLF0u60HDyQLKZ5+GE5zpNPLlxurlRWgS+JrAV+f2beA8BRAM4iogO8KzDzjcw8kZknDh06NOPm5Ji0BP7yy4Hnn09nW374RTT2fWZX1KMKfJyIxuzDZvVqqdww9/1YtUoE3DuhthkyePHi0uMZZmD6dLlv6vDtTtYs6uDTqoEH0hH4adNkdqtPfrJwuZ3BK7HJVOCZ+SPndgmAewDsleX+ujVpdbJedlnXf7KorF1b/Gd6kGu34xpAhHTZskLh9pZJ2g4+akQDdBX4NWukmqR37/CIxhvPAG7H38KFpQm8aZM5brsqJksHn1aJJOBGNKWWSq5bJ/Xvn/501xOoRjSJyEzgiagPEfUz9wEcDmB2Vvvr9lRDRHPQQcCll4avExTRmBODeX6rreSYFi4sfL09zV+pEY2fwPfrJ39hEY1fOWFSgTftMW01VUNxHXwlBT6pg3/0UXmtXT1jUIFPRJYOfgsAzxPRawD+A+AhZn40w/11b6pB4BcsAN5/P3ydYhGNLfCAe1GUwbj4NCMaI/CbbRYe0fg5eBPRLFqUzMHbAm9fgWtOXt5faEnr4OfNA4YOddufhKQCP3UqMGQIcECXBFe2XV+vAl8imdXBM/N7AHbLavuKh2oQ+I6O4v/kxTpZvQL/0UeFr+/slJ/xpooESCeiKSbwq1cDAwd2XW5P+pGkRNJENESF7Tadths2FFbomIgqSUSThnsH3HaVEtGsXy/xzKmnFl7zYNDxaBKhZZJ5IY0MPulJIonAex38yJFy63VuplQyrYimrU1eG8XBh0U0QDoRDVD4yyMo307ayZpWDTzgtrEUB//YY3Jy9ItnDDoeTcmowOeFNBx80pNER0dxFxc3ovESFNG0tXUd6jeKwJvMfbPNimfwYRENkE4nq91uc6ET0FU8k9TBd3ZKlJa2gy9F4KdNkw7uAw8MXkcFvmRU4PNCGgIfNBZ6nNcX+ycvVkVjhMoIrhcj8N4qGsDf5QLhEY19BWqxiCZLB+8n8FEcfCkCv3ChvD4tB19qRLNhg0zuceKJ7nH4oWPCl4wKfF6IIvArVoQ/Xw6BjxrRNDTIZete/By8EUFvTBPHwYcJfFubHFdYmaT3flT8Lr6yT0xB7jhJRJNmBQ1QekQzfbq8/96Lm7yogy8ZFfi8UCxeef11YPBgYMqU4HXKLfBhEU0cgW9slFuvg4wr8H4RzZtvAjNnyv0sIhpvezo7s3fwaV7kBJQe0UybJh3XhxwSvp52spaMCnxeKObg58yR20dDKlXTEPg4GXxYRFOKgy8lojGO3Tj4jRsLTzynnQac4Yyy4RfR9O7tbjeNgcba2ws7Wf3Es7NT1iu1Dt44+FGjEjcXQGkXOm3cCNx3H3D88eHxDKAOPgGZDheslJFiAm+uEAwT8awdfFC1CODv4LfYous2bIE34mYcfJKIZrPNXAG3Bx97/333dX4OnkiiGVNqGRdvRNPRUdjJ6iee9vOl1MHPmycnT7PtpDQ0SIljHAf/5JMi2mHVM4YBA+T97eyMP9ZPN0ffrbxQTODNP0aYiCepomGW169fH7wd775LiWiCyiSB5BGNEXjj6ltbZfAv03fhJ/CAG9OkEdFEcfD2bFalRDRp1sAb4o4JP3WqvJ/F4hlA1mMOrnBSAlGBzwvFxNk4+LD1kjh4e7t+NelAVxGyha1SEY03g7eXLVokt6bCJWjmI9O5Wi4Hb06GpWbwadbAG+JM29faCtx7L3Dcce5nGIYOOFYyKvB5oZiDv+MOuc0qorFfG/SPHhYjxI1o7DLJNCIaPwe/YEHh9oIcfBKB97anvb2w3WEOvmdPOab6+ugRTUcH8MEH6Tv4ONP2PfWUiHWUeAbQ8WgSoAKfF4oJ/O23y20UgS8l57S3G/SPHuYy0yiTLCWiWb3a7az0Crx3oLMsIho/B+8X0djH5v1l0tAQ3cF/9JGcRCoZ0UybJu/VYYdFW1/HhC+ZWJ2sRLSZ/RpmLlJYrZSNqBc6VavA+0U0fvMDdHa6VSRpRTRGmL0RjdfBZxHReLEdvB3RBDl4cxvVwZsKmiwimigC39YG3HOPxDPml1cx1MGXTCSBJ6JvAvgJgA2QWZrg3G6bUbuUuETtII0i8EEzH0XdbloOfvPNu67X2VmYQQPFIxrT/2COy36v1qxxhTssojFzv/qRZkTjdfCmQiUtB592DbwhaMA3L888I53WxS5ustEMvmSiOvjzAXyCmZdl2RglAWk4eCN8SR180D96XIHv2VMuzlq+vLCNZt0oEU1DgyvsxRx8mMD37x984uvTx50bNi7ebXodPNA1304i8PPnyz633jp+W8Noair8nIKYOlVOiEccEX3b6uBLJup/8rsAEszHpWROVIGPUkVTLRENAFx9ddf9RBV4c7WnoZjA9+kj6/hl8EHxDCCDZQ0aVNovH297vA4e6FqhYpdJAvEjmuHDo1WvxCFKRNPeLvHMMcfEOxmqwJdMVAd/CYB/E9FLADYVLzPzdzNplRKfWsjg41bRAHIl6Ve+4q7X2dlV4MIiGvsqyaBO1iFD5H5dnXvREtDVwQdx4YXAKacEPx9GuR38vHnpxzNAtIjm2WdlGsao1TOGxkb5rFXgYxNV4G8A8BSA1wGkNPmnkippZvBhAv/b34pAHH988HbjOngzgYe9jt/kD4B/RNPQINsIimgMQQ7e7nC0BxyLKvDDhslfqXgdvPck53Xw3j6Inj3jRTSlzrkbRhQHP3WqrHfkkfG3r+PRlERUgW9g5u9n2hIlGeVy8Oee678/+wQTN4Pv1SvYwXvxE3jA30G2thZuxxyXV+Dt+MUI/MaNhZlyWESTBD8H741oojj4KBFNWxvw4YfZOPhiAt/RAdx9N3D00YUzU0VFx6Mpiai/xR8hojOIaBgRDTJ/mbZMiUeYwHsdYhCVyuB79QrO4L0ECXxjo39EEyeDB0TI16xxr2I1YhTm4NPEeyWraUMaVTTNzfL+VSKiee45YMmS+PGMQceEL4moDv5U5/YSa5mWSVYTYQJv//OXo4qmFIEvxcHb4u0nMMUims7OrnOp9ukjy0w8s/XWwFtvZSvwYWPRAHJstriVWgefVQ08ICehtrau77lh2jQ5jsmTS9u+OviSiCTwzJzBN0JJlbAM3na2aVXReEf2SyLwjY3liWiMwJt9tLSIuNoC39Agy43AjxolAl+uiCbIwZtfFEDpDj6rGnig8Ipb72fX0QHcdZeIuz1+fhz69+86AbtSlEhWjYgaiOi7RDTN+fsOERUZxFkpK2EO3h61Ma2IZpnnkogkY9HYEY0Z2Cuok9WvTBIoPEkYvBHNhAlye889cmvKIO0xb+rrpQ3mOVMvXmkHHzSapFkvisDPny+fbdB8t0kIGxP+3/+WE1Sci5u8qIMviai/xf8AYAKA652/Cc6yohBRDyJ6lYgeLK2JSiTCBN528GkJvO0ovdtN6uDr64Nryv3KJM39YmWSEyYA++0H/O530t7335fltqM1Ar9ggdw34+FkJfBBDr6uzv0cvB2YSSKakSOLT7BRCmGzOk2dKp/x0UeXvn3N4Esiaga/JzPvZj1+iohei/jacwDMAZDRb1wFQPkFfuFCYNw4/+0mzeDDBCisk9Xr4Neu7Vqxcc45wGc/Czz0kHT6AYUzG9kCP2yYK6JZRTSA/2iSYf0L3j6IOBFNFvEMECzwnZ0Szxx5ZLKxevr3l+isvT34153ShagOvoOItjMPiGhbAEXHliWikQCOBvCn0pqnRCYNgTf5fJQrMufODd5u0iqaUgTez8EvXtx1RMoTT5SI4pprxNH26AGMGOE+X18vbTACb8SknFU03r6DKA4+akSTRQcrEBzRvPCCvJelVs8Y9GrWkogq8BcAeJqIniGif0IuejovwuuuAXAh9OKo7AnrPE0zgzdO9pxz5JJzv+3GrYP3RjSlOnivwC9a1HVM+fp64KyzgKefBh5+WCIL2xE2NLgZ/PDhblvKFdH4Ofi+feWYzftaSh18a6t0UpbbwU+bJu20vyuloAJfEpEEnpmfBLADgO8COBvAGGZ+Ouw1RHQMgCXM/HKR9c4gohlENGPp0qURm610IaqDT1pFY48h8uSTXV8LlC+isUXQ3gYgYrh6tf+Y8t/4hhzHq692nXjajmiGD3fFP8uIxn7v/Bz82LFyO2uW3La2yonBjJIZJaL54AP5jpRT4Ds7ReCPOCL5+6djwpdE5CoaAN8EcKnz940IVTT7ATiOiOYDuAPAwUT0N+9KzHwjM09k5olD/cb/VqJRrgw+aD+Vjmi8Dn7xYrn1E/hBg4Avf1nuewWvvl6y+xUrJKIph4O33xc/B7/nnnL73//KrRlEzbj/KBFNljXwgH9E85//yMVVSeMZQB18iWRWRcPMlzDzSGYeDeAUAE8x8xcTtFUJY9KkrqWLBtvZmjJEP4xIv/deeBWLoaHBfWxeGzZ1W1CMECeisUdb9Gbw9nGaKh+/af8A4LvOOHnbeq7Vq693J9kePlzG3PnZz7IpLTR4L0TzOviRI2VsfK/AG6JENFnWwAP+Dn7aNGnbsccm374KfEmUo4pGKRfvv++OjGhju+s4k277VSwwA4ceCowZA/z+9+KUhw1zX9u3b+WqaGwHbwTez8EDwC67yNygdiUQUHi8w4dLB+wPfhDcnqQEOXjvBVp77lko8PaxR4lo/DqU08Qr8Mwi8Icf7sYrSdBJP0oi0yoaAzM/w8wJe1mUophM1kvUsWi84u/XWcoM7LQTcNRR8viDDwq3269faYONxY1oevQoPF6vgw+LaAwHHSQTith4Bb4c2L+q/CIaQAT+rbdknByvg49SBz9/vly0lVWJoTeimTFDDEeSi5tsNIMviagCfz5Kq6JRyklSgfc+5yfUnZ3iKE1k4SfwpTp45ugC752wws/BE/nP6xqGLYBJhgCOQ7GIBhCBZwZeeUWO3xvRFHPwWdbAA10d/NSp8l56h5UuFdNJqwIfi6KncyLqAWA3SBXNGGfxXGbeGPwqpSKkLfB+Qs0snbDmEv60BN48H1XgvQ63Vy9xvx0d8j4sWiTuPO5Vm0bgGxq6uvssICp032EOHpCYxs/Bd3R0HR/IZv5891dXFphJV9atc+OZQw8FBg5MZ/sNDXISUYGPRVEHz8wdAE5l5o3MPMv5U3GvRoL+uW2Bj5PB+wm1cfD9+4uYf/hh4WuNwPtV25hL8L0YcWhtTebgATemWbQoPJ4Jwgj8sGGljapZCsXKJAH5JTJqlL/Am3WDTqAbNkhdf5YOnsjtYH/lFfnFkEb1jI1O+hGbqN/gfxHRdUT0SSLaw/xl2jIlPuWIaJjln9lM3GzGc7EFntk/E25t9Z/swYj1xo3RBN7byejdBuB/FWsUbIEvB0EXOvm9BxMnlibwfmPuZIEZt37aNPkuphXPGHQ8mthE7XEZ79xebi1jAAen2holGUE16llENIAIqBnPxa6iMa/1inBbmyxbu7ZwuVfgjRsPamOYgzc5/KJFMrBYXIzAl6uD1Ytx8H5193vuKeO6DBzYNaIBggU+6xp4Q1OTjBfz2GPAwQenH3HpiJKxiToe/EFZN0RJgaD4Jep0ft7Xh0U0gJRkvvqq3LcdvHmtN38NcqZ2vJJGRMPsP0xBFMy+yyXwcRy8yeFfew3Yf393uVk3qJLGCHzWDr53bxl75t13gYsuSn/7/fsDH3+c/nZzTKjAE1HoPKzM/Ot0m6N04dFHgdmzgfPPL75uUoEvFtH88IeyzBZ4c3GVn8B7CRIue7KIUgXePN6wQUoJ169PFtFU0sH7dbIC7nj2HR3xIpp582SdrGOnpiZg5kyJZ044If3t9+/vXrClRKJYBt/P+ZsI4FsARjh/ZwLQDL4cHHUUcMEF0daNEtGEERbRLFsGXHGF3DcRzeDB4qg6OtzXmhl77Jr0FStc4cpK4G0HH6UGPohyZ/BezIQffu9B//5ygRngH9GEOfhRo4L7aNLCfI4HHhi/PDUKmsHHJtTBM/NPAICIngWwBzOvcR7/GMBDmbdOiUfaDt4I/EsvyVAIBtvBM7siD7gCb7Lw1lZgu+2Aq68uLvDr1iUrkzT7NVO7leLCy+3ggyb88HPwgHS0zp0b38FnHc8A7sVOaV3c5EUz+NhEraLZAoBtD1qdZUo1kVVE88ILhcuNgzfDIixb1lXgzWtXrZLStoULy+fg77hDxGDffYO3E0SlI5owBw+4OXzcTtasO1gB+Rzr6mTM/Szo319O4N6JXZRAolbR3ArgP0TkTGaJEwDckkmLlNLJKqJZvbpwue3gAX+BNw6+pUVu29q6lvcZ4gp8WJnk8uXA3XcDn/98eDVOEJMmyeTQO+wQ/7WlEOTg4wh8WCfrunVS6VQOB3/00fJrrZTO7SjYA45tvnk2+8gZUatoriCiRwB80ln0VWZ+NbtmKSWRVRVNkMCbMjhb4E2ZpFfgw6pD4kQ0xcok//EP2ecXSxy4dNw4mc6vUgRdyWoYP16y9KgRTbkqaADgm9/Mdvv2eDQq8JGIM/JQE4DVzHwzEQ0lom2YWbu0q4msIpo1awqXR4lojMCbmvewYQjSrKK55x7pULTLCGuJoCtZDU1N0tk9caK7LCyiKVcNfDnQIYNjE0ngiegySCXNGAA3A2gA8DfIpB5KtZBVJ2uxiGb5creDLSyiKUcG39oq7r1cwwwkJcqUfV68NeZhEU05HXzWqMDHJup/wYkAjgPQAgDMvABSPqlUE+XO4JuaRNjDOlnTjmiKOXig9HimGmhvl784g6SFRTTz5sl7k1UuXk50TPjYRBX4VmZmyPAEIKI+2TVJKZmkDv6JJwofG4EPimgA92KnJBGNPZZ4W1v4mOVBZZLGwU+cKOPV1wq2g29ocE+MYQ7eS7GIZvTo2vlFE4aOCR+bqJ/6nUR0A4ABRPQNAE8AuCm7ZildCBsFstg6UQT+o4+Af/2rcJkRmyAHD0hHa5wqGj+Br68XkVq7VtpaShXNZpvJxTXf+U7wa6udxkb3PS/FwQdFNHmIZwCNaEogahXNVUR0GIDVkBz+UmaenmnLlEI6Ooq7sCQRjdelA8ERTRwH741ogpxp797ufkqpounVS2rts75aM23sk2Xv3u77lmZEY4Y4qHXMMBgq8JGJXEXjCPp0IhoCYHl2TVJ8iZLLJnHwfq8tlsEDkp/bAt/QICIepZPVzERkxhKPIvB+87Eaak3cvdgOvpSIxuvg16yRDvA8VNAA8vn266cZfAxCLSERTXKm6bubiHYnotkAZgNYTERHlqeJCoDwYX4NaQt8UERjU1cnrzXt69GjUKhMBu/XyWqEiahwtp4wgTcnHT+Br3WSRjReB5+nChqDjkcTi2IO/joA/wegP2Qe1qOY+UUi2gnA7QAezbh9isGemDmIJBFN0FWQQPh8n7bAm4lA7PlRwxx8z57u81EdvBHAvAi8/WuosdF9z9PoZM1TDbxBx6OJRbFO1npmfpyZpwJYxMwvAgAzv5V907oBa9YUThQdRtYO3m98j6C5VW1sgTcRSVSBN/fjRDSlRBi1QtqdrHl08CrwsSgm8LZieOdvi1h7pwSy2WbA2LHR1k1T4P0E30/g16/336/9+qgCHxbRABLRqINPN6KZN0/e1yyG7q0UKvCxKBbR7EZEqwEQgN7OfTiPQ0dyIqJGAM8C6OXsZxozX5awvfnjvfeirecX0XiFOqrAd3R0rTX3E/hly/zLDm2naATerpCxBd6ug/cONmbfj+rgzXbzIvCGurr06+BNiaT3atlaZsAAGS5ZiUSx8eCTlCVsBHAwM68logYAzxPRIybmUWJSzEn7PQ5a7ifwQVHRH//YdZl9MjACb198ZJf7+Tn4n/xEJgF59NHCq2KjdLLm1cHX18tf2hFNnuIZQB18TOIMNhYL58pXM7tyg/OnsU6p2A5+6lTgs58F3n67cJ2oDr69vVAge/UKng3ID/tkYATedud21GAEvrVVTiwNDcCll8qycePc7fTuXVhqGUTeBN7Q0FBY5hnHwZvX+UU0++yTvG3VhBF45nz9MsmITK9fJqIeRDQTwBIA05n5JZ91ziCiGUQ0Y+nSpVk2p/Z4yXq7bAc/ZYrcvvFG4fpxIhqbMHH3G8MkSOCN6PpFNH7OtGdP95/UDFfgXceGKL9lkg0Nhb+q4jh4InkvbYFfuVL+8lRBA4jAt7VFL07o5mQq8MzcwczjAYwEsBcRfcJnnRuZeSIzTxyap86gNLCnybNF2YimuWrUYIR8yhTgllu6LjdEKbk8+mgR7vvu6/pcFAfvjWiMMHuraOyIxl5uc9BBMoFHXV3+HLwd0ZTq4AF5z+wT9fvvy23eIhozHo1e7BSJsoxAxMwrATwNQC+OKhVblI3A26IIuA7+xhvlz1DMwfvRo4eIz957Ax9+WPhcWgLv7WQ1eAX+qaeA224rFPi8lUkmcfBAVwc/z5mqIY8OHtAcPiKZCbwzKcgA535vAIcB0Pr5UunoEGF/8klX4L0iYAS+paXQ4ZTi4O3xZrxiWkzg/TpZgwQ+TkRTV5e/KpogBx9X4L0OPo818IAKfEwy62QFMAzALUTUA3IiuZOZH8xwf/mmvR0480xxsoagKpp16worXZ56qnC9KA7eFvihQ0WAzInBr4rGr5O1o8N13H5XaNoiFhbRGHr0yF9EY/A6+FIiGq+D79sXGDQonfZVCyrwsciyimYWgN2z2n63o6Oja828t1PVdvDG5U+fDtx7b+F6URy87SaJgE99Sn49ANEjGvtKWL9REktx8HkW+CQO3hvRzJ8v8UzeKk00g49FDmYB6CZ0dLjDpRqCLnRat05Evr0dmDNHlpnp9cy2grZh8A5NfOCB7n0/gbfr4I3Am3jGJkjgozj4PAq8tw7ekLSTNY818IA6+JiowFeKqLMsGdrb5Se3jdfBM8ufEdZVq9x1bPGwHbwt9v36AUcdJfe9An/yye79KA6+tdUdY95ut99YNEB0B5/nMsmkGbxx8MwS0ajAd3tU4CtFlBmabDo6ZOyasG0YoTXLV60qHMbX3lZHB/CtbwHPPecu79XLdZTesdV32gl45hm5H6UOHpCxyAH3nxJIHtGYE2NeBN4cf5pVNB9/LCfXvFXQAGIW6upU4COSZSerEkaUjk6b9vZoEY0di6xc6Yq9Ldjt7cA998gwBCarBwoF3m/2qJEj5bZYJ6sRayPwAwbIlIBA8ojGfm2eSLMOPq8VNIB8X/r31ww+IurgK0WUjk4bvwzez8HbHZt2RGOLY3s78Mtfyv1XXnGXhzl4wHXmUSIaQAYrA9yOMSC4iiaqgwekjXEdbrWShYPPaw28QcejiUw+Bf5f/wJuvbVy+2cWATVXE/oR18GHdbIec4z7OIqDf+IJYMYM6Xh98013eTEHb2KRJAKfNKLxtjMvqIOPjgp8ZPIp8NdfD1x4YeX2v2IFcPHFwN13B68T18G3t7vCaTj4YLn9hDMChJ+D98vgf/lLGWPm4osLt1dM4AcNkmqav//dXRYm8HZEY7DF+9BDZQgCIHodvL39PGE7eKL488vanazz54sI2u97nlCBj0w+M/i1a8U9dnb6C1XWGIfrN8a6oRQHH4QRg7AM3n4fVqwAfv5zYOLEwu00NoZHNHV1wNNPd11WqsBPnix/QHwHnxfsiMa856X0L3gjmrzGM4B8n8J+HSubyKfAt7SIIK5aBQwcGO01r78OLFgAHHFE9P289hqwaFHX1xhhDxP4Uhx8UGmlEQbmrg7evMasc8EFwJgxwBe+0LVOvZiD9yNoPHiguMDbxOlkzZPAG+w6+FL6F7wRzQ47pNa0qkMdfGTyK/AAsHRpdIE3Y5PHqU+//HIpHVy2rDATNsIeNgxvKZ2sQRhh8HPwffu6k2EDwBe/6B6rHXWMGgXst597YVRcgfcrkzQZfFCZpI06ePckXKrAt7W5NfCHHZZeG6sNFfjI5DODN6V/RmCy4sMPJe7w/lyM4uBLiWiKOXi/DL6tTU4ARhyDtnHttcAVV3TdZjGMwHd2+ney9upVeCIpJvB1dcEnF7M87xl8kohm2TL5DuQ5orEn/VBCyafA2w4+CqWeCExt98svFy43GXyaDj5uRDNwoDj4jRsLoxdvaaU3FiglojH4ZfB9+gQPEWxjHGyYe82zg7eraJJENHmvoAEk8uvsLLyGQ/FFBR6QLD0u7e2SvwOFteRAdg4+CL9O1uHDxeV4Bd57kjDxiRHesE5WP8IEftmyrgIfNltT795d54r121eeBN6Q1MGbiMbUwOdZ4HW4gsjkW+CjOvOZM+XWW2cexuLFrhv2OvgsMvgwB29n8MbBDx8ul6wXE3gz/EEWDt6MnxP1Ap7evcOfz3OZZFIHbyKa7uDgVeAjkz+Bt0UuroOPM2Vgc7PcbrGFCLwtnJV28D16yD94c3N0B2/XYAPJBN7uMI3q4AGppOmuEY3t4JNGNIMGdR23KE+owEcmfwK/fr0rYnEdfBxM/n7ssbIfe1q7LMoko3Symgy+Tx9g++3lBLd0aTSBN8PwphnRAPEEvpiDz6PAG9Kqg897DTygY8LHIH8Cb5cJRnHwGze6pYFxMAJ//PFya8c0lYxoWlrECW+/vSx7881wgTdlpKbDKs2IBogX0XRnB59WHXxex4G3Mabk1Vcr244aQAX+zTdFPJuaXEFmLv7ajz4SQTv4YHFefgKfZkRz3XXARRfJ/T59Cp/zlkn26QNst50sa24OF/jrrwdOPx040pkP3Zx4orpIW+CN8Np5chYOPo8ZfFIH39CQ73HgbbbbDthnH+CHPwQ++1lgyZJKt6hqya/ANzZGi2hM/r7vvu5r774b2Hpr6aQM4qOPpCOzqQnYZZfsHfw777j3r7uu8Dk7ojEO3gg8EC7ww4YBf/6zK86m/8I7uUgQQUP4GhHu06fQwYdVyXT3iCaJgzfv/caN+Y9oGhqAZ5+V4Tbuuw8YOxa4806ti/chfwJvoobRo6M5+JkzRVjGj3fFbe5cqWU3l9r70dwMjBgh9ydMKOxoTWssmjAxtDFiajv4vn2BLbeU5WEC78W8B95fCUEECbzpaLUdfEND+CiQxToH8yjwfhFNqQ7ekHcHD8h7dcklUqK8zTbA5z4HfOYzUt2mbCJ/Am9c+OjRct90Hgbx2mty6X6/fiLIHR3uTz57WFwvH31UKPBLl7qVNXEdfJDoBv2je9tlBO+ii4DHH3fF2bj4OAJv3r+kAm9OOn37Fgp8GP/v/wG33FJ8X3mPaErN4A3dQeANY8cC//43cOWVwAMPyOM77lA375BvgQfCYxpmcfC77eYKWkuL6/yDBJ65q8AD7gVPcatogtx80D/66tWFj72Odu5cud12W/f5cjt4v4immHBtvbUMhFZsX3l38EkiGqB7CTwg79tFF0mn63bbAaeeCpx0knshYjcmM4Enoq2I6GkiepOI3iCic7LaVwFG4EeNktuwmOaDD6TUavz4QoEv5uBXrRIhNFPY7babiI/J4aM4eFvU0xb4Aw+UWzNCY6UFPqqDL4Z5n/Ik8IY0OlkBuZYj6meXN3bZRSb7+dWvgIcfFjf/9793azefpYNvB3AeM+8CYBKAs4holwz3J8Rx8KaD1evgiwm8KZE0Dt7b0ZqWgw/6R1+zpvCxHVmsXAn86U+Fz/fqVXywMUPaAh8noimGGe88TwKf1oVO5r3PewdrMerrZUjsmTOBHXeUYbFPPBFYuLDSLasImQk8My9k5lec+2sAzAEwIqv9bcJ0skZx8DNnyj/Yrrv6C3xQfu8VeKCwozVumaSfwI8YIePT++G9gq+xEfj+94EXXpAaYa8Ahg025iXLiCbpRNlG4POYwdulpUkcfHeLZ4LYaSfg+eeBq64CHntM3Pzf/tbt3HxZMngiGg1gdwAv+Tx3BhHNIKIZS6MOLWDT2iodcy85m47r4LfbTjpYTZyxdm3xDN50pnoFfvFiEeW4nax+Ah8k7kDXq0x79gSuvhqYNKlwuRH1UjpZSymTtE8sQVU0STDvZ54cvCGNoQoAFXibHj2A884TI7fzzsCXviQXJob9b+WMzAWeiPoCuAvAucy82vs8M9/IzBOZeeLQOGPBuBuQD/Gqq+RxS4v8o2yxhXzAxRz8+PFy3zjW5mZXcItFNMOHu8tMR+vLL6fj4MP45S+BX/zCfVzM8cURePN8mhFNEuGyyXNEk9TBa0QTzJgxUjf/618D06eLm7/11m7h5jMVeCJqgIj7bcwcMgN1Anr1Ar7yFeDee8VBt7SIONXVAYMHBzv41auB996T/B1wBc0MtwqEC/yQIYVCY3e0xh0PPq7A9+9fOGF2MeGMI/CGauxkzXNEk1YGrw7enx49gO99D5g1SyapP+00GUfKmLWckmUVDQH4M4A5zPzrrPYDADjjDBHMm292BR4QEQ5y8LNmya3XwZvhVoFwgTcVNIY+fST3sx18e3tw5m0LvD0LUxS8FwsFOT676iSuwNtzpIZRCYHPo4NPWgf/yU8CP/qRW0Gl+LPDDsA//wlccw3w1FPi5v/yl9y6+Swd/H4AvgTgYCKa6fxNzmRPO+4oX+ybbpIKEyPWQ4cGC7xdQQP4O/iwTlY7fzeYjlY7mgly8bZrX7063hcsqsCbdpQi8EkGGwNKq4MvRh4F3pD0StamJpkjOI+/btKmrg445xwxeePGAV/9KnD00W7fWo7IsormeWYmZh7HzOOdv4ez2h/OOEMilyeeKHTwQRHNzJlyabxx4nEiGnuYApsJE+TiCvtXwFlnAc8913XdCy5w77/wgnzpnnrKf3/FCBLOJAIflUqUSeZJxNJy8Ep8tt8eeOYZ4He/E1c/diwwZUqu3Hx+rmQ98UTJ3FescCtAijn43XZz/8GMwJsJtHv08Bf4jRtlm34Cv8MOcmsPDDZlCvDb3xau19FROJDZP/8ptz/5SfDx2UR18HbVSVSBnz5dLhSJihH4urrC6h6/KpqkZZLdpYom6fukRKeuDvjOd4DXXwd23x342teAo44qnN+hhsmPwDc2SscJUBjRrFjRtROTGZg9W36eGXr2lH+w1lY5UTQ1+Qu8uWDCT+DNRATe/T39dGEW761jN2L/7LOluYc0Hfyhhxb+uiiGEXivKA0dKuWn9jyrGtEEk3TKPiUZ224rv6B//3upnx87Vi4YrHE3nx+BBySmAQojms7OrsP+Ll8u+bq3pMy8bvPN5YThJ/B+FzkZzOQZXlasKJzY29se86sBEDdhjiMIr4MPEoRSHHxcgsaH+fa3ZWyQujodqiCMtK5kVZJTVyff29dfByZOBL7xDeCII2RIkxolXwI/Zgxw9tnSYQK4c6x6YxrTmeKthDGVI0OHisD7dbKuWFG4bRvj4IGuFyM9+aR73zvVmBH4XXeVjuI//7nrtm28Ah80BG85M3ivg7fHpK+rk/2nJVx5yuANSevglfTYZhvpy/vDH6R/7BOfAG68sSbdfL4EHgCuvVZmKAJcEfZ2tAYJfBQHb0TfnljaYAt8v37u/d69CwXe6+DXrZNx0GfNEtd9TsC4bKakMyq2wEcdiyYuQQLvpaEhPYHPq4M33z/7u6NUhro64Mwzxc3vtRfwzW8Chx9eWEBRA+RP4G2GDJFbr4M3HSilCLxZ5uciGxtdobMnrjjmGMnXTWRiC7zpELZPDqec4ns4eOopGfs6Kn4RTbGxaOKiAp8ODQ1yZfSTTwKf/nSlW6MYRo+WwoMbbgBefFF+Zf/xj+n/H2VEvgU+zMH36OHOeGSwBb537/gCT+Tm8GZiYEBmmlm3zh0vx45ozPp2fr/nnv7HM3CgzEUZlUpGNF7q6zWiCcPk7wcfnM8TWC1DJP1is2fL/9+3vgUcdlhhSXWVkm+BD3Lwzc0yF6k3J08a0QCuE7d/Zh96aGGdu3Hwa9a469sCb2fqc+b47ycKtoM34+ZEHUQsKlEFvnfv4PcsLnkSQDuiUaqbUaNkZMqbbgL++19x89dfX9VuPt8C36uXCK2fwG+1Vdf17fLKUiIawBVsO6IZOBDYYw/5+T13rtzW18v+zHp2RGOz007+y6NgO/hrrpGhHNK+lD2qwP/lLzIWSBrkUQzzeEx5hAj4+tfFze+/v1zIeMghcpFlFZJvgQdErP0iGm/+DnR18H5VNGZZkIv0c/CAfAlefBH4/Odl3tSdd5YvixH4oBLLMI46KlxYbQff1CSDsoVNel0KUafRO+IIuXIwDdI+hkpijyap1A5bbw088ohUvL3yirj5666rOjeff4H3DjjGHF3ggxx8z57BY7X4OXhAstW2NvkyXHqpO/uTyepLEfiHHw4fkth28FkR1cEr4aiDrz2IpGLvjTeAT31KSrQPOgh4991Kt2wT+Rd4r4NfubJwPlWbqJ2sYVmyEWrj4I1D239/VwRPO839hw6LaMysVKWSN4HfY4/s91Fu1MHXPiNHAg89JBHoa6+Jm7/22qpw8/kXeK+DD6qBB0RQN99cxDaskzWsisPr4M1tU5PENJ/6lFwWbQiKaJYskZwvCeUYu6WcAv/cczKYWx5RB1/bEEkE+sYb8mv9nHOkv+vttyvarPwLvNfBhwn8WWcB//ufiFZYRBNF4I3g2cI9dSrw4IOF6wc5+KFDk1e8GAeRF4FvapKZuvKICnw+GDECeOABmUb09ddlQMNrrok/qU9KdA+BX7/enWs0TODr691M3K+TdfFiGbs9LKIxQr3ddnIWf9gaIblPn66inSSDj0peBD6PaESTP4iAL39Z3Pwhh0j12Kc+JeaxzORf4L218M3N8gEMGxb+usZGiTiMC37jDYlW7r033MEboe7dW87cO+8cvp8oVTRJxTNL8VCBT8aAAfId8F6TodQ+w4cD998P/PWvwJtvipv/9a/L6ubzL/Deq1mbm+UK1mI/iY1L37hROmU/9zl3ar0oEU1U1zx2rPyD27m8l4ULk80dmWVZoQp8Ms48U0bdzFPpp+JCBHzxi2IQDz8cOO88mV5x7tyy7D7/Au/n4P3iGS9GxDdsAL7/ffmAdtxRloVFNHvsIR9k1IHB9txTxof3DptgM2iQeyVqHP7xj+zHNTHOM09Xl5aTpqbwk7uSD4YNk1//t90m4r7bbsBVV2Xu5vMv8H4OPo7A/+1vMtDQhRe6E4oEzbMKyAnlscfCBbtcfPazwF13ZbsPdfCKEg0iudDxjTfkIsULLpDy6STDkRSh+wh8qQ7+vPOAvfcGfvYzqW8Fqvay5IqgAq8o8dhyS+Duu4Hbb5cyyt13B375S6C9PfVd5V/gN9tM8valS6UCZvXqeALfu7d8EA0NMvA/4E7bp6jAK0opEMmw4G+8IcOJ33prJgKf/9osIolNli1zOyqjCLyJWG66yZ3az1xZamZ+UlTgFSUJW2wBTJsmI8xmMAx2ZgJPRFMAHANgCTN/Iqv9RMJczWom+vAbSdLLAQcACxYUllPW1Um+Pnp0Js2sSVTgFSU5GV0Hk2VE8xcAR2a4/eiYq1nDLnLyElQrf/jhbjWNogKvKFVMZgLPzM8CWJHV9mMxdKg4eCPwpZQcKv6owCtK1VLxTlYiOoOIZhDRjKXeiTnSwkQ0zc0ymJjWbKdH1PHgFUUpOxUXeGa+kZknMvPEoaakMW2GDpVOjPnzo8UzSnQGDhRx1/dVUaqO/FfRAG4t/GuvAZMmVbYteWPwYBnC155kXFGUqqB7CLwZrmDJEnWaWRA0n6yiKBUls4iGiG4H8AKAMUTUTERfy2pfRbGjHxV4RVG6CZk5eGY+Nattx8Y4eEAFXlGUbkPFO1nLgjp4RVG6Id1D4AcPdu+rwCuK0k3oHgLf0OB2BI4YUdGmKIqilIvuUUUDSExTV6cDhSmK0m3oPgI/ZEj4TEyKoig5o/sI/EUXAW1tlW6FoihK2eg+An/88ZVugaIoSlnpHp2siqIo3RAVeEVRlJyiAq8oipJTVOAVRVFyigq8oihKTlGBVxRFySkq8IqiKDlFBV5RFCWnEDNXug2bIKKlAN4v8eVDACxLsTmVIi/HAeixVCN5OQ4gP8eS9DhGMbPvhNZVJfBJIKIZzDyx0u1ISl6OA9BjqUbychxAfo4ly+PQiEZRFCWnqMAriqLklDwJ/I2VbkBK5OU4AD2WaiQvxwHk51gyO47cZPCKoihKIXly8IqiKIqFCryiKEpOqXmBJ6IjiWguEb1DRBdXuj1+ENEUIlpCRLOtZYOIaDoRve3cDnSWExFd6xzPLCLaw3rNac76bxPRaRU4jq2I6GkiepOI3iCic2r4WBqJ6D9E9JpzLD9xlm9DRC85bf4HEfV0lvdyHr/jPD/a2tYlzvK5RHREuY/FaUMPInqViB6s8eOYT0SvE9FMIprhLKu575fThgFENI2I3iKiOUS0T9mPhZlr9g9ADwDvAtgWQE8ArwHYpdLt8mnnAQD2ADDbWvYrABc79y8G8Evn/mQAjwAgAJMAvOQsHwTgPed2oHN/YJmPYxiAPZz7/QD8D8AuNXosBKCvc78BwEtOG+8EcIqz/I8AvuXc/zaAPzr3TwHwD+f+Ls73rheAbZzvY48KfMe+D+DvAB50HtfqccwHMMSzrOa+X047bgHwded+TwADyn0sZT3gDN7AfQA8Zj2+BMAllW5XQFtHo1Dg5wIY5twfBmCuc/8GAKd61wNwKoAbrOUF61XomO4DcFitHwuAJgCvANgbckVhvff7BeAxAPs49+ud9cj7nbPXK2P7RwJ4EsDBAB502lVzx+Hsdz66CnzNfb8A9AcwD04hS6WOpdYjmhEAPrQeNzvLaoEtmHmhc38RgC2c+0HHVFXH6vy03x3ifGvyWJxYYyaAJQCmQ1zrSmZu92nXpjY7z68CMBjVcSzXALgQQKfzeDBq8zgAgAE8TkQvE9EZzrJa/H5tA2ApgJud6OxPRNQHZT6WWhf4XMByaq6ZelUi6gvgLgDnMvNq+7laOhZm7mDm8RAHvBeAnSrbovgQ0TEAljDzy5VuS0rsz8x7ADgKwFlEdID9ZA19v+ohsewfmHl3AC2QSGYT5TiWWhf4jwBsZT0e6SyrBRYT0TAAcG6XOMuDjqkqjpWIGiDifhsz3+0srsljMTDzSgBPQ6KMAURU79OuTW12nu8PYDkqfyz7ATiOiOYDuAMS0/wWtXccAABm/si5XQLgHsiJtxa/X80Ampn5JefxNIjgl/VYal3g/wtgB6dioCek0+j+CrcpKvcDMD3ip0HybLP8y06v+iQAq5yfdI8BOJyIBjo974c7y8oGERGAPwOYw8y/tp6qxWMZSkQDnPu9IX0JcyBCf7KzmvdYzDGeDOApx4HdD+AUpzplGwA7APhPWQ4CADNfwswjmXk05Pv/FDN/ATV2HABARH2IqJ+5D/lezEYNfr+YeRGAD4lojLPoEABvotzHUu5OlAw6MyZDqjneBfCDSrcnoI23A1gIoA1yZv8aJPd8EsDbAJ4AMMhZlwD83jme1wFMtLZzOoB3nL+vVuA49of8pJwFYKbzN7lGj2UcgFedY5kN4FJn+bYQYXsHwFQAvZzljc7jd5znt7W29QPnGOcCOKqC37MD4VbR1NxxOG1+zfl7w/w/1+L3y2nDeAAznO/YvZAqmLIeiw5VoCiKklNqPaJRFEVRAlCBVxRFySkq8IqiKDlFBV5RFCWnqMAriqLkFBV4pWYhog5n1MHZRDSViJoC1vt3Svs7gYgude7/mIjWEdHm1vNrA9r2gFVzX+eMGjibZNTE/zp15yCiJ8zogoqSBirwSi2znpnHM/MnALQCONN+0lzJycz7prS/CwFcbz1eBuC8CG1bAeAsZ/nnAAwHMI6ZdwVwIoCVznN/hYz2qCipoAKv5IXnAGxPRAcS0XNEdD/kykGvs77Icc6vEdGVzrLtiOhRZ4Cr54ioy5g0RLQjgI3MvMxaPAXA54hoUJG2vQB3gKhhABYycycAMHMzM3/sPHc/ZPRARUkFFXil5nGc+lGQKwABGfPjHGbe0bPeUQCOB7A3M+8GGZsbkEmPz2bmCQDOR6FLN+wHGVLYZi1E5M8JaVsPyGXqZgiNOwEc68Q3VxPR7mZdR+h7EdHgIoesKJFQgVdqmd7OcL8zAHwAGScHAP7DzPN81j8UwM3MvA4AmHmFMzLmvgCmOtu6AeKyvQyDDP/q5VoAp5kxVHzaZoaEne7ssxnAGMj4650AniSiQ6zXLYFEOIqSmPriqyhK1bKeZbjfTch4aGiJsY06yNjp44ustx4y8mIBzLySiP4ON2MvaJvT8fuY8/y1zms2QmbveYSIFgM4ATI+CSBjxayP0X5FCUQdvNKdmA7gq6bahogGsYxnP4+IPuMsIyLazee1cwBsH7DdXwP4JnwMk/Nr4bsAziOieiLag4iGO/uqgwx69r7ZN4AtIbMaKUpiVOCVbgMzPwrJwmc48cn5zlNfAPA1IjKjGB7v8/JnAezuiLB3u8sgY5f3CtivGbXyVACbA3iAZAL2WQDaAVznrDoBwIvszsSkKInQ0SQVJSJE9FsADzDzExlu/35mfrLoyooSAXXwihKdn0Mm6M6K2SruSpqog1cURckp6uAVRVFyigq8oihKTlGBVxRFySkq8IqiKDlFBV5RFCWn/H9kmhlpchxfvwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "price_area_median = housing.groupby('Area(sq ft)').median()['Price']\n",
    "price_area_median\n",
    "# Create figure and subplot\n",
    "fig = plt.figure()\n",
    "ax1 = fig.add_subplot(1,1,1)\n",
    "\n",
    "# Create plot with median data\n",
    "ax1.plot(price_area_median.index, price_area_median, color='r', label='Median')\n",
    "ax1.legend(loc='best')\n",
    "\n",
    "# Adding labels to the axis and title\n",
    "props1 = {'title':'Median Price of Houses',\n",
    "         'xlabel':'Price (NRS)',\n",
    "         'ylabel':'Bedroom'}\n",
    "ax1.set(**props1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'Median Price of Houses'),\n",
       " Text(0.5, 0, 'Area(sq ft)'),\n",
       " Text(0, 0.5, 'Price (NRS)')]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "price_bath_median = housing.groupby('bathroom').median()['Price']\n",
    "price_bath_median\n",
    "# Create figure and subplot\n",
    "fig = plt.figure()\n",
    "ax1 = fig.add_subplot(1,1,1)\n",
    "\n",
    "# Create plot with median data\n",
    "ax1.plot(price_bath_median.index, price_bath_median, color='r', label='Median')\n",
    "ax1.legend(loc='best')\n",
    "\n",
    "# Adding labels to the axis and title\n",
    "props1 = {'title':'Median Price of Houses',\n",
    "         'xlabel':'Area(sq ft)',\n",
    "         'ylabel':'Price (NRS)'}\n",
    "ax1.set(**props1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'Median Price of Houses'),\n",
       " Text(0.5, 0, 'Bedroom'),\n",
       " Text(0, 0.5, 'Price (NRS)')]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "price_bed_median = housing.groupby('floors').median()['Price']\n",
    "price_bed_median\n",
    "# Create figure and subplot\n",
    "fig = plt.figure()\n",
    "ax1 = fig.add_subplot(1,1,1)\n",
    "\n",
    "# Create plot with median data\n",
    "ax1.plot(price_bed_median.index, price_bed_median, color='r', label='Median')\n",
    "ax1.legend(loc='best')\n",
    "\n",
    "# Adding labels to the axis and title\n",
    "props1 = {'title':'Median Price of Houses',\n",
    "         'xlabel':'Bedroom',\n",
    "         'ylabel':'Price (NRS)'}\n",
    "ax1.set(**props1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1299, 7)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "strat_train_set.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(325, 7)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "strat_test_set.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_tr=strat_train_set.drop(\"Price\", axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_price=strat_train_set[\"Price\"].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_te=strat_test_set.drop(\"Price\", axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_price_te=strat_test_set[\"Price\"].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Selecting a desired model and training it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "model = LinearRegression()\n",
    "#model = DecisionTreeRegressor()\n",
    "#model = RandomForestRegressor()\n",
    "model.fit(housing_tr, housing_price)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "some_data = housing_tr.iloc[:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "some_labels = housing_price.iloc[:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([26530653.13574686, 19157486.05696311, 31699858.98899912,\n",
       "       26430150.12230903])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(some_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[31000000, 13600000, 19200000, 28500000]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(some_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluating the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "housing_predictions = model.predict(housing_tr)\n",
    "mse = mean_squared_error(housing_price, housing_predictions)\n",
    "rmse = np.sqrt(mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6580689099702728"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmse/iqr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6928875.044311407"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "mse/iqr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Saving the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['project.joblib']"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from joblib import dump, load\n",
    "dump(model, 'project.joblib') "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Testing the model on test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "\n",
    "final_predictions = model.predict(housing_te)\n",
    "final_mse = mean_squared_error(housing_price_te, final_predictions)\n",
    "final_rmse = np.sqrt(final_mse)\n",
    "# print(final_predictions, list(Y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "NRMSE=final_rmse/iqr\n",
    "NMSE=final_mse/iqr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Normalized Mean Squared Error and Normalized Root Mean Squared Error is 7081576.449104621 and 0.6652807888922081 respectively.\n"
     ]
    }
   ],
   "source": [
    "print(\"The Normalized Mean Squared Error and Normalized Root Mean Squared Error is\",NMSE,\"and\",NRMSE,\"respectively.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Result Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plot\n",
    "plot.plot(housing_predictions,housing_predictions, color='green')\n",
    "plot.scatter(housing_price,housing_predictions)\n",
    "plot.title('Actual Price vs Predicted Price(Train Data)')\n",
    "plot.xlabel('Actual Price(blue)')\n",
    "plot.ylabel('Predicted Price(green)')\n",
    "plot.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plot\n",
    "plot.plot(final_predictions,final_predictions, color='green')\n",
    "plot.scatter(housing_price_te,final_predictions)\n",
    "plot.title('Actual Price vs Predicted Price(Test Data)')\n",
    "plot.xlabel('Actual Price(blue)')\n",
    "plot.ylabel('Predicted Price(green)')\n",
    "plot.show"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\milan\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([29781596.])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from joblib import dump, load\n",
    "import numpy as np\n",
    "model = load('project.joblib') \n",
    "features = np.array([[3,3.0,1,13.0,5,1755.75]])\n",
    "y=model.predict(features)\n",
    "y.round(0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['bathroom', 'floors', 'parking', 'roadsize(feet)', 'Road type',\n",
       "       'Area(sq ft)', 'Price'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 5, 3.0, 2, 13.0, 5, 1754.03)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "3,5,3.0,2,13.0,5,1754.03"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "def features(bathroom, floors, parking, roadsize,\n",
    "    Road_type, Area):\n",
    "    x = np.array([ bathroom, floors, parking, roadsize,\n",
    "    Road_type, Area])\n",
    "    prediction = model.predict(x.reshape(1, -1))\n",
    "    return f\"RS. {prediction}\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gradio as gr\n",
    "outputs = gr.outputs.Textbox()\n",
    "a=gr.inputs.Dropdown([\"1\", \"2\", \"3\",\"4\",\"5\"])\n",
    "app = gr.Interface(fn=features,description =\"This model predicts the price of house in Kathmandu valley.\\n\\n\\n\\nRoad Type Index (Select a value as shown below)\\n\\n1=Blacktopped\\n\\n2=Concrete\\n\\n3=Gravelled\\n\\n4=Paved\\n\\n5=Soil Stabilized\", inputs=['number','number','number','number',a,'number'], outputs=outputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running on local URL:  http://127.0.0.1:7860/\n",
      "\n",
      "To create a public link, set `share=True` in `launch()`.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"900\"\n",
       "            height=\"500\"\n",
       "            src=\"http://127.0.0.1:7860/\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "            \n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x2367108ff70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(<fastapi.applications.FastAPI at 0x2365fccfca0>,\n",
       " 'http://127.0.0.1:7860/',\n",
       " None)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\milan\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\milan\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\base.py:566: FutureWarning: Arrays of bytes/strings is being converted to decimal numbers if dtype='numeric'. This behavior is deprecated in 0.24 and will be removed in 1.1 (renaming of 0.26). Please convert your data to numeric values explicitly instead.\n",
      "  X = check_array(X, **check_params)\n",
      "C:\\Users\\milan\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\milan\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\base.py:566: FutureWarning: Arrays of bytes/strings is being converted to decimal numbers if dtype='numeric'. This behavior is deprecated in 0.24 and will be removed in 1.1 (renaming of 0.26). Please convert your data to numeric values explicitly instead.\n",
      "  X = check_array(X, **check_params)\n",
      "C:\\Users\\milan\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\milan\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\base.py:566: FutureWarning: Arrays of bytes/strings is being converted to decimal numbers if dtype='numeric'. This behavior is deprecated in 0.24 and will be removed in 1.1 (renaming of 0.26). Please convert your data to numeric values explicitly instead.\n",
      "  X = check_array(X, **check_params)\n",
      "C:\\Users\\milan\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\milan\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\base.py:566: FutureWarning: Arrays of bytes/strings is being converted to decimal numbers if dtype='numeric'. This behavior is deprecated in 0.24 and will be removed in 1.1 (renaming of 0.26). Please convert your data to numeric values explicitly instead.\n",
      "  X = check_array(X, **check_params)\n",
      "C:\\Users\\milan\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\milan\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\base.py:566: FutureWarning: Arrays of bytes/strings is being converted to decimal numbers if dtype='numeric'. This behavior is deprecated in 0.24 and will be removed in 1.1 (renaming of 0.26). Please convert your data to numeric values explicitly instead.\n",
      "  X = check_array(X, **check_params)\n",
      "C:\\Users\\milan\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\milan\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\base.py:566: FutureWarning: Arrays of bytes/strings is being converted to decimal numbers if dtype='numeric'. This behavior is deprecated in 0.24 and will be removed in 1.1 (renaming of 0.26). Please convert your data to numeric values explicitly instead.\n",
      "  X = check_array(X, **check_params)\n",
      "C:\\Users\\milan\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\milan\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\base.py:566: FutureWarning: Arrays of bytes/strings is being converted to decimal numbers if dtype='numeric'. This behavior is deprecated in 0.24 and will be removed in 1.1 (renaming of 0.26). Please convert your data to numeric values explicitly instead.\n",
      "  X = check_array(X, **check_params)\n",
      "C:\\Users\\milan\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\milan\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\base.py:566: FutureWarning: Arrays of bytes/strings is being converted to decimal numbers if dtype='numeric'. This behavior is deprecated in 0.24 and will be removed in 1.1 (renaming of 0.26). Please convert your data to numeric values explicitly instead.\n",
      "  X = check_array(X, **check_params)\n",
      "C:\\Users\\milan\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\milan\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\base.py:566: FutureWarning: Arrays of bytes/strings is being converted to decimal numbers if dtype='numeric'. This behavior is deprecated in 0.24 and will be removed in 1.1 (renaming of 0.26). Please convert your data to numeric values explicitly instead.\n",
      "  X = check_array(X, **check_params)\n",
      "C:\\Users\\milan\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\milan\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\base.py:566: FutureWarning: Arrays of bytes/strings is being converted to decimal numbers if dtype='numeric'. This behavior is deprecated in 0.24 and will be removed in 1.1 (renaming of 0.26). Please convert your data to numeric values explicitly instead.\n",
      "  X = check_array(X, **check_params)\n",
      "C:\\Users\\milan\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\milan\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\base.py:566: FutureWarning: Arrays of bytes/strings is being converted to decimal numbers if dtype='numeric'. This behavior is deprecated in 0.24 and will be removed in 1.1 (renaming of 0.26). Please convert your data to numeric values explicitly instead.\n",
      "  X = check_array(X, **check_params)\n",
      "C:\\Users\\milan\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\milan\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\base.py:566: FutureWarning: Arrays of bytes/strings is being converted to decimal numbers if dtype='numeric'. This behavior is deprecated in 0.24 and will be removed in 1.1 (renaming of 0.26). Please convert your data to numeric values explicitly instead.\n",
      "  X = check_array(X, **check_params)\n",
      "C:\\Users\\milan\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\milan\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\base.py:566: FutureWarning: Arrays of bytes/strings is being converted to decimal numbers if dtype='numeric'. This behavior is deprecated in 0.24 and will be removed in 1.1 (renaming of 0.26). Please convert your data to numeric values explicitly instead.\n",
      "  X = check_array(X, **check_params)\n",
      "C:\\Users\\milan\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\milan\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\base.py:566: FutureWarning: Arrays of bytes/strings is being converted to decimal numbers if dtype='numeric'. This behavior is deprecated in 0.24 and will be removed in 1.1 (renaming of 0.26). Please convert your data to numeric values explicitly instead.\n",
      "  X = check_array(X, **check_params)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\milan\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\milan\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\base.py:566: FutureWarning: Arrays of bytes/strings is being converted to decimal numbers if dtype='numeric'. This behavior is deprecated in 0.24 and will be removed in 1.1 (renaming of 0.26). Please convert your data to numeric values explicitly instead.\n",
      "  X = check_array(X, **check_params)\n",
      "C:\\Users\\milan\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\milan\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\base.py:566: FutureWarning: Arrays of bytes/strings is being converted to decimal numbers if dtype='numeric'. This behavior is deprecated in 0.24 and will be removed in 1.1 (renaming of 0.26). Please convert your data to numeric values explicitly instead.\n",
      "  X = check_array(X, **check_params)\n",
      "C:\\Users\\milan\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\milan\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\base.py:566: FutureWarning: Arrays of bytes/strings is being converted to decimal numbers if dtype='numeric'. This behavior is deprecated in 0.24 and will be removed in 1.1 (renaming of 0.26). Please convert your data to numeric values explicitly instead.\n",
      "  X = check_array(X, **check_params)\n",
      "C:\\Users\\milan\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\milan\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\base.py:566: FutureWarning: Arrays of bytes/strings is being converted to decimal numbers if dtype='numeric'. This behavior is deprecated in 0.24 and will be removed in 1.1 (renaming of 0.26). Please convert your data to numeric values explicitly instead.\n",
      "  X = check_array(X, **check_params)\n",
      "C:\\Users\\milan\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\milan\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\base.py:566: FutureWarning: Arrays of bytes/strings is being converted to decimal numbers if dtype='numeric'. This behavior is deprecated in 0.24 and will be removed in 1.1 (renaming of 0.26). Please convert your data to numeric values explicitly instead.\n",
      "  X = check_array(X, **check_params)\n",
      "C:\\Users\\milan\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\milan\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\base.py:566: FutureWarning: Arrays of bytes/strings is being converted to decimal numbers if dtype='numeric'. This behavior is deprecated in 0.24 and will be removed in 1.1 (renaming of 0.26). Please convert your data to numeric values explicitly instead.\n",
      "  X = check_array(X, **check_params)\n",
      "C:\\Users\\milan\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\milan\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\base.py:566: FutureWarning: Arrays of bytes/strings is being converted to decimal numbers if dtype='numeric'. This behavior is deprecated in 0.24 and will be removed in 1.1 (renaming of 0.26). Please convert your data to numeric values explicitly instead.\n",
      "  X = check_array(X, **check_params)\n",
      "C:\\Users\\milan\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\milan\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\base.py:566: FutureWarning: Arrays of bytes/strings is being converted to decimal numbers if dtype='numeric'. This behavior is deprecated in 0.24 and will be removed in 1.1 (renaming of 0.26). Please convert your data to numeric values explicitly instead.\n",
      "  X = check_array(X, **check_params)\n",
      "C:\\Users\\milan\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\milan\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\base.py:566: FutureWarning: Arrays of bytes/strings is being converted to decimal numbers if dtype='numeric'. This behavior is deprecated in 0.24 and will be removed in 1.1 (renaming of 0.26). Please convert your data to numeric values explicitly instead.\n",
      "  X = check_array(X, **check_params)\n"
     ]
    }
   ],
   "source": [
    "app.launch()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "language_info": {
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